





'''simple docstring'''






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
					from ...processing_utils import ProcessorMixin
					from ...utils import TensorType


lowerCAmelCase									=   logging.get_logger(__name__)

lowerCAmelCase									=   {
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}
class 		lowerCamelCase	(   _A							):
	snake_case_   = "layoutlmv3"



	def __init__( self		,   a_=50_265		,   a_=768		,   a_=12		,   a_=12		,   a_=3_072		,   a_="gelu"		,   a_=0.1		,   a_=0.1		,   a_=512		,   a_=2		,   a_=0.02		,   a_=1e-5		,   a_=1		,   a_=0		,   a_=2		,   a_=1_024		,   a_=128		,   a_=128		,   a_=True		,   a_=32		,   a_=128		,   a_=64		,   a_=256		,   a_=True		,   a_=True		,   a_=True		,   a_=224		,   a_=3		,   a_=16		,   a_=None		,   **a_		,   ):
			super().__init__(
			    vocab_size=a_		,   hidden_size=a_		,   num_hidden_layers=a_		,   num_attention_heads=a_		,   intermediate_size=a_		,   hidden_act=a_		,   hidden_dropout_prob=a_		,   attention_probs_dropout_prob=a_		,   max_position_embeddings=a_		,   type_vocab_size=a_		,   initializer_range=a_		,   layer_norm_eps=a_		,   pad_token_id=a_		,   bos_token_id=a_		,   eos_token_id=a_		,   **a_		,   )
			lowerCAmelCase  :     Any       						= max_ad_position_embeddings
			lowerCAmelCase  :     Tuple       						= coordinate_size
			lowerCAmelCase  :     Union[str, Any]       						= shape_size
			lowerCAmelCase  :     Optional[Any]       						= has_relative_attention_bias
			lowerCAmelCase  :     Union[str, Any]       						= rel_pos_bins
			lowerCAmelCase  :     Optional[int]       						= max_rel_pos
			lowerCAmelCase  :     Any       						= has_spatial_attention_bias
			lowerCAmelCase  :     Tuple       						= rel_ad_pos_bins
			lowerCAmelCase  :     List[str]       						= max_rel_ad_pos
			lowerCAmelCase  :     Optional[Any]       						= text_embed
			lowerCAmelCase  :     Union[str, Any]       						= visual_embed
			lowerCAmelCase  :     Tuple       						= input_size
			lowerCAmelCase  :     List[str]       						= num_channels
			lowerCAmelCase  :     List[Any]       						= patch_size
			lowerCAmelCase  :     Dict       						= classifier_dropout






class 		lowerCamelCase	(   _A							):
	snake_case_   = version.parse("1.12"							)



	@property
	def 	_lowerCamelCase     ( self						):
			# The order of inputs is different for question answering and sequence classification
			if self.task in ["question-answering", "sequence-classification"]:
					return OrderedDict(
					    [
					        ("input_ids", {0: "batch", 1: "sequence"}),
					        ("attention_mask", {0: "batch", 1: "sequence"}),
					        ("bbox", {0: "batch", 1: "sequence"}),
					        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
					    ]						)
			else:
					return OrderedDict(
					    [
					        ("input_ids", {0: "batch", 1: "sequence"}),
					        ("bbox", {0: "batch", 1: "sequence"}),
					        ("attention_mask", {0: "batch", 1: "sequence"}),
					        ("pixel_values", {0: "batch", 1: "num_channels"}),
					    ]						)



	@property
	def 	_lowerCamelCase     ( self						):
			return 1e-5



	@property
	def 	_lowerCamelCase     ( self						):
			return 12





	def 	_lowerCamelCase     ( self		,   a_		,   a_ = -1		,   a_ = -1		,   a_ = False		,   a_ = None		,   a_ = 3		,   a_ = 40		,   a_ = 40		,   ):
			setattr(processor.image_processor		,   "apply_ocr"		,   a_						)

			# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
			lowerCAmelCase  :     Dict       						= compute_effective_axis_dimension(
			    a_		,   fixed_dimension=OnnxConfig.default_fixed_batch		,   num_token_to_add=0						)
			# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
			lowerCAmelCase  :     Union[str, Any]       						= processor.tokenizer.num_special_tokens_to_add(a_						)
			lowerCAmelCase  :     Union[str, Any]       						= compute_effective_axis_dimension(
			    a_		,   fixed_dimension=OnnxConfig.default_fixed_sequence		,   num_token_to_add=a_						)
			# Generate dummy inputs according to compute batch and sequence
			lowerCAmelCase  :     Any       						= [[" ".join([processor.tokenizer.unk_token]						) * seq_length]] * batch_size

			# Generate dummy bounding boxes
			lowerCAmelCase  :     int       						= [[[48, 84, 73, 128]]] * batch_size

			# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
			# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
			lowerCAmelCase  :     List[Any]       						= self._generate_dummy_images(a_		,   a_		,   a_		,   a_						)

			lowerCAmelCase  :     Any       						= dict(
			    processor(
			        a_		,   text=a_		,   boxes=a_		,   return_tensors=a_		,   )						)

			return inputs










'''simple docstring'''






from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst






def   __A    (				):
		lowerCAmelCase						,		lowerCAmelCase  :     List[Any]       						= 9, 1_4  # noqa: F841
		lowerCAmelCase  :     int       						= [
		    [0, 1, 4],
		    [0, 7, 8],
		    [1, 2, 8],
		    [7, 8, 7],
		    [7, 6, 1],
		    [2, 8, 2],
		    [8, 6, 6],
		    [2, 3, 7],
		    [2, 5, 4],
		    [6, 5, 2],
		    [3, 5, 1_4],
		    [3, 4, 9],
		    [5, 4, 1_0],
		    [1, 7, 1_1],
		]

		lowerCAmelCase  :     Optional[Any]       						= defaultdict(a_						)
		for nodea, nodea, cost in edges:
				adjancency[nodea].append([nodea, cost]						)
				adjancency[nodea].append([nodea, cost]						)

		lowerCAmelCase  :     int       						= mst(a_						)

		lowerCAmelCase  :     Tuple       						= [
		    [7, 6, 1],
		    [2, 8, 2],
		    [6, 5, 2],
		    [0, 1, 4],
		    [2, 5, 4],
		    [2, 3, 7],
		    [0, 7, 8],
		    [3, 4, 9],
		]

		for answer in expected:
				lowerCAmelCase  :     List[str]       						= tuple(answer[:2]						)
				lowerCAmelCase  :     int       						= tuple(edge[::-1]						)
				assert edge in result or reverse in result



